SaND

Traditional information discovery methods are based on content: documents, terms, and the relationships between them. In the Web 2.0 era, people join the equation, creating documents and tags in many forms.

Searches that incorporate personalization, social graphs, content, and personalized recommendations are just some of the tasks that can take advantage of this newly formed ecosystem.

Social Networking & Discovery (SaND)
is an aggregation platform for information discovery and analysis. It leverages the complex relationships between content and people that surface through social applications. Its integrated index supports the combination of content-based analysis and people-based analysis over a rich data foundation.

The cornerstone of the SaND
platform is its ability to aggregate information from different sources for any entity while preserving its relations with other entities. For example, a blog post representation will include all the text in the post (including comments), and will have connections to the blog author and every commenter, as well as to tags used on the post either internally or through a tagging system.

Through its aggregation model, SaND
supports queries over any entity in the system, be it a textual term, a person, or a tag; it retrieves a ranked list of entities related to that entity. More specifically, it can find the social network of a person based on a flexible set of relationships. SaND
supports a generic model that can easily be extended with new data sources, entity types, and relationships.

SaND
can be utilized in various applications. Search applications based on SaND enable a unified search over multiple types of entities, and can include advanced features such as viewing the relationships between the retrieved entities (as depicted in the figure below), and personalization based on a user's activity and social network profile as reflected in SaND.

The SaND framework can support many other applications, such as personalized recommendations and content, social paths, expertise location, reputation systems, and more.

Research Goals

Exploit ways to use the aggregated data to improve existing methods for discovery of information and people, and devise new algorithms for both discovery and recommendation of entities and the relations between them.